Salinity stress in Eruca sativa (E. sativa)

#install.packages("corrplot")
#install.packages("dendextend")
#install.packages("docxtractr")
#install.packages("dplyr")
#install.packages("Formula")
#install.packages("ggfortify")
#install.packages("ggplot2", dependencies = TRUE)
#install.packages("ggpubr")
#install.packages("Hmisc")
#install.packages("lattice")
#install.packages("readxl")
#install.packages("survival")
#install.packages("tidyr")
#install.packages("tidyverse")

library(corrplot)
library(dendextend)
library(docxtractr)
library(dplyr)
library(Formula)
library(ggfortify)
library(ggplot2)
library(ggpubr)
library(Hmisc)
library(lattice)
library(readxl)
library(survival)
library(tidyr)
library(tidyverse)

Introduction

Under abiotic stress such as salinity, several morphological and physiological parameters of plants are affected. As function of their location, plants from the same species can exhibit different tolerance to abiotic stress. In the study we will review, 25 accessions of Eruca sativa from different regions of Pakistan were analyzed. Each accession have been separated into two groups according to the received treatment: first group underwent salt stress (150 mM NaCl) during two weeks and the second was the control. Following that 19 morpho-physiological traits were measured.

The data was obtained from the paper "Evaluation of salt tolerance in Eruca sativa accessions based on morpho-physiological traits" (Afsar et. al 2020)

Objectives and hypotheses

Our main objectives are:

1. Explore the effects of salt stress on various morpho-physiological traits in different E. sativa accessions.
2. Identify traits the salt stress has the biggest influence on.
3. Define the susceptible and resistant accessions.
4. Compare our results with the ones from the paper.
5. Explore if location can be linked to salt-tolerance.

Main hypothesis:

• We expect to observe statistically significant differences in morpho-physiological traits between control and salt-treated plants.
• Salt-tolerant and salt-susceptible accessions will belong to different groups based on how much salt stress affects morpho-physiological parameters.
• Geographical location and/or elevation may impact adaptation to salinity.

Methods

What have you done? And what did you use to do it? Step by step, so anyone could do it again by reading this part.

1. Downloading and tidying the data.

First we had to download the data.xls file from the paper. The tables needed to be tidied (column names organized, empty cells filled, the values formatted, etc.). Then we had to download the location of accessions.doc data and tidied this table as well.

In order to be able to work with all of morpho-physiological parameters we had to unify them (because of different number of replicates in different categories of morpho-physiological traits measures). The unification was done trough the mean in every accession and treatment. With unified data we created a summary table (Table 1).

2. Test of normality and statistical difference of variables.

Once we had the summary table, we tested the normality of variables with Shapiro–Wilk test. Our Null Hypothesis (H0) was that morpho-physiological parameters' distribution was normal. The results for every group of morpho-physiological traits were summarized in tables (Table 2-6).

If the H0 was accepted (p > 0.05) we used ANOVA, otherwise (p < 0.05) we proceeded with Kruskal-Wallis test to see if there was a significant difference between salt-treated and control plants for each variable. The parameters' distributions and results of normality tests were visualized in the boxplot figures (Figure 2.1-2.19)

Additionally, we calculated mean and standard deviation values of treated and control plants and summarized them into tables (Table 2.1-2.5).

3. Correlation analysis.

In order to see the degree of association between the morpho-physiological parameters (and, additionally, elevation) we performed Pearson correlation analysis. The results were visualized in the Figure 3.1??.

We started doing Pearson correlation using membership function values (MFV) of the 11 selected salt tolerance traits, the results were very similar to that in the article. But we discarded that analysis as we didn't understood the utility of it.

Instead we computed Pearson correlation on the means of all salt tolerance traits (treatment and control), as it shows (prior to calculating MFV) which values has positive correlation and which has the negative, thus allowing us to choose the appropriate formula for MFV calculation (see explanation of MFV calculation in the next section).

4. Salt tolerant index (STI)

5. membership function value (MFV) calculation.

In order to evaluate degree of salt tolerance (define the susceptible and resistant accessions), first we needed to convert all morpho-physiological parameters (control and treatment) for each accession to the salt-tolerance index (STI) - ratio of treated plant values to control.

Here we deviated from the

we needed to rank the We created another table with data converted into salt tolerance index (STI): .

The data of all morpho-physiological parameters (control and treatment) for each accession was converted to the salt-tolerance index (STI) which is the ratio of the value for the NaCl-treated plant/value for the control. For the categorization of E. sativa accessions according to their salt tolerance, membership function value (MFV) was applied as previously described (Chen et al., 2012). The MFV was calculated according to the following formula:

Then, we computed the MVF table using the formula provided in the article: \(Xp=[(X−Xmin)/(Xmax−Xmin)]×100%\). For the traits inversely related to salt tolerance (Electrolyte leakage, Na, K, Ca) we used the second formula: \(Xp=[1−(X−Xmin)/(Xmax−Xmin)]×100%\) For calculating MFV, we used the same formula, however we used the min and max of the mean STI.

We computed the mean of MFV from MFV values of the variables above. With the results, we ranked the accessions from highly tolerant to highly sensitive.

We performed the Principal Component Analysis on the MFV values of all the variables, except for RWC and ICO2 because they did not show significant difference between treatment and control. We used 8 highest ranked variables from the results: PH, SL, RL, NL, DW, FW, LA, K_Na.

We still need to make the dendogram, add map with accession and check for correlation between altitude and MFV ranking.

5. PCA

6. Salt-tolerance evaluation: ranking and dendogram

In order to evaluate salt tolerance, we made accessions groups that behave similarly under salt stress. This was based on the MFV mean of the 8 highest ranked physio-morphological parameters from PCA analysis.

The ranking was done by ordering MFV mean from highest MFV mean to the smallest one. Then the groups were adressed according to the same logic as the paper (with higher MFV mean corresponding to higher salt tolerance): 4% accessions as highly tolerant, 16% as tolerant, 60% as moderately tolerant, 16% as sensitive and finally 4% as highly sensitive.

The dendogram was established based on MFV mean.

7.Accessions map and location correlation ?

Results

(Figures and tables with captions and description of what do they mean.)

1. Data download and transformation.

###----Data download and transformation----###

##Downloading the .xlsx data
#download excel sheet from internet and store it into an Excel object
Excel <- "https://dfzljdn9uc3pi.cloudfront.net/2020/9749/1/Raw_data_Afsar_et_al.%2C_2020-PeerJ_20.5.2020.xlsx"

#create the folder "data" if it doesn't exist
D <- "data"
if (!dir.exists(D)) dir.create(D)
f <- paste0 (D, "/data.xlsx")

#download excel into the folder "data" (created beforehand) under the name "data.xlsx"
download.file(Excel, f, mode="wb")

#get the names of the excel sheets
excel.sheet <- excel_sheets(f)

##Tidying the .xlsx data
#create a list with the 4 sheet table from excel to tidy it further
X <- list()
for (i in excel.sheet){
  X[[i]] <- as.data.frame(read_xlsx(f,i))
}

#organize column names and fill the first 2 columns
for(i in names(X)){
  names(X[[i]])[1:3] <- c("Number", "Code", "Treat_Contr")
  X[[i]] <- X[[i]][-1,]
  X[[i]] <- fill(X[[i]], 1:2)
}

#transforming columns' data format 
for (i in seq_along(X)){
  X[[i]][2:3] <- lapply(X[[i]][2:3], as.factor)
  X[[i]][-(2:3)] <- lapply(X[[i]][-(2:3)], as.numeric)
}

#creating data frames from different .xlsx sheets
Morpho_t <- X$`Morphological traits`
Weight_ion <- X$`FW DW RWC Ions EL`
Chloro_c <- X$`Chlorophyll content`
Gas_e <- X$`Gas Exchange parameters`

#changing names of the columns for more convinient use
names(Morpho_t)[4:8] <- c('Shoot_Length', 
                          'Root_Length', 
                          'Plant_Height', 
                          'Number_Leaves', 
                          'Leaf_Area')
names(Chloro_c)[4] <- c('Chlorophyll_Content')
names(Weight_ion) [4:12] <- c('Fresh_Weight', 
                              'Dry_Weight', 
                              'RWC',
                              'Na',
                              'K', 
                              'Ca', 
                              'Mg', 
                              'K_Na', 
                              'Electrolyte_Leakage')
names(Gas_e) [4:7] <- c('Photsynthesis_Rate', 
                        'Intercellular_CO2', 
                        'Transpiration_Rate', 
                        'Stomatal_Conductance')

#adding NA cells into the missing 7th and 16th accession
#creating a data frame with the missing data first
Gas_e_miss <- data.frame (Number = rep(c(7, 16), each = 8),
                          Code = rep(c("Es-7", "Es-16"), each = 8),
                          Treat_Contr = rep(c("Treatment", "Control",
                                            "Treatment", "Control"), each = 4),
                          Photsynthesis_Rate = rep(NA, 16),
                          Intercellular_CO2 = rep(NA, 16),
                          Transpiration_Rate = rep(NA, 16),
                          Stomatal_Conductance = rep(NA, 16))

#changing the numbers in "Number" column
Gas_e$Number <- replace(Gas_e$Number, , rep(c(1:6, 8:15, 17:25), each = 8))

#adding NA cells into the missing 7th and 16th accession
if (!(is.element('Es-7', Gas_e$Code))) Gas_e <- rbind(Gas_e, Gas_e_miss)

#sorting the variables according to Number
Gas_e <- Gas_e[order(Gas_e$Number),]

#rewriting row names to correspond to the ordered data
row.names(Gas_e) <- c(1:200)

##Downloading the .docx data
#downloading data for the location of accessions
word <- "https://dfzljdn9uc3pi.cloudfront.net/2020/9749/1/Table_S1.docx"
w <- paste0(D, "/location.docx")
download.file(word, w, mode="wb")

##Tidying the .docx data
#creating data frame for the location of accessions
Acc_loc <- docx_extract_all_tbls(read_docx(w, track_changes = NULL),
                                 guess_header = TRUE, preserve = FALSE,
                                 trim = TRUE)
Acc_loc <- as.data.frame(Acc_loc)

#removing m from Elevation column 
Acc_loc$Elevation <- sub(' m', '', Acc_loc$Elevation)

#transforming data in the accession_loc
Acc_loc[c(1,6)] <- lapply(Acc_loc[c(1,6)], as.numeric)
Acc_loc[-c(1,6)] <- lapply(Acc_loc[-c(1,6)], as.factor)

#In order to fit the length of this table to the length of the ultimate table
#we need to double all the rows
Acc_loc_double <- Acc_loc[rep(1:25, each = 2), ]

## Creating the summarized table to work with
#create summary table for morphological trait
a <- Morpho_t %>% 
  group_by(Number, Code, Treat_Contr) %>% 
  summarise(Shoot_Length = mean(Shoot_Length),
            Root_Length = mean(Root_Length), 
            Plant_Height = mean(Plant_Height), 
            Number_Leaves = mean(Number_Leaves), 
            Leaf_Area = mean(Leaf_Area))

# remove NA values from Weight_ion table
Weight_ion <- na.omit(Weight_ion)
#Create summary table for Weight ion
b <- Weight_ion %>% 
  group_by(Number, Code, Treat_Contr) %>% 
  summarise(Fresh_Weight= mean(Fresh_Weight),
            Dry_Weight= mean(Dry_Weight),
            RWC = mean(RWC),
            Na =mean(Na),
            K =mean(K),
            Ca =mean(Ca),
            Mg= mean(Mg),
            K_Na =mean(K_Na),
            Electrolyte_Leakage= mean(Electrolyte_Leakage))
#take only the columns number 4 to 12 to avoid repetition of number, code and treatment
b <- b[4:12] 

#Create summary table for Weight_ion
c <- Chloro_c %>% 
  group_by(Number, Code, Treat_Contr) %>% 
  summarise(Chlorophyll_Content = mean(Chlorophyll_Content))
#take only the column number 4 to avoid repetition of number, code and treatment
c <- c[4]  

#Create summary table for Gas_e
d <- Gas_e %>% 
  group_by(Number, Code, Treat_Contr) %>% 
  summarise(Photsynthesis_Rate= mean(Photsynthesis_Rate),
            Intercellular_CO2= mean(Intercellular_CO2), 
            Transpiration_Rate= mean(Transpiration_Rate), 
            Stomatal_Conductance=mean(Stomatal_Conductance))
#take only the columns number 4 to 7 to avoid repetition of number, code and treatment
d <- d[4:7]

#add Acc_loc data to the table
e <- Acc_loc_double
e <- e[6]

#create data frame with all the summarized tables 
table <- data.frame(a, b, c, d, e)

##Creating the caption for the summary table
knitr::kable(table, caption = "Table 1.1 Mean values of salt tolerance traits for each accession and treatment type", align="c" , digits = round(2))

Table 1.1 Mean values of salt tolerance traits for each accession and treatment type

	Number	Code	Treat_Contr	Shoot_Length	Root_Length	Plant_Height	Number_Leaves	Leaf_Area	Fresh_Weight	Dry_Weight	RWC	Na	K	Ca	Mg	K_Na	Electrolyte_Leakage	Chlorophyll_Content	Photsynthesis_Rate	Intercellular_CO2	Transpiration_Rate	Stomatal_Conductance	Elevation
1	1	Es-1	Control	1.89	2.40	4.16	4.20	0.05	343.10	20.93	75.84	4.60	8.72	4.02	2.74	1.90	51.07	16.88	12.41	286.0	3.16	0.22	14
1.1	1	Es-1	Treatment	1.69	1.54	3.08	2.60	0.03	171.97	18.06	89.81	21.50	11.55	3.51	2.54	0.54	48.42	13.65	4.62	245.5	1.64	0.08	14
2	2	Es-2	Control	2.83	1.93	4.99	4.40	0.08	173.13	11.97	71.60	6.04	8.81	4.28	3.49	1.46	61.86	21.17	2.74	271.5	0.90	0.04	17
2.1	2	Es-2	Treatment	1.54	1.27	2.53	2.80	0.04	58.84	9.00	91.74	23.86	12.99	3.74	2.83	0.55	62.49	15.20	1.78	288.5	1.12	0.04	17
3	3	Es-3	Control	3.51	2.35	4.62	4.40	0.16	321.27	15.93	76.90	7.44	9.59	5.69	3.79	1.31	64.60	18.67	17.00	290.5	4.18	0.31	579
3.1	3	Es-3	Treatment	0.69	0.45	1.19	1.33	0.01	32.76	3.93	89.22	17.00	14.46	5.74	3.55	0.85	78.67	13.20	1.34	258.0	0.56	0.02	579
4	4	Es-4	Control	3.56	2.75	6.05	4.60	0.10	347.55	19.72	96.03	7.08	6.94	2.69	5.11	0.98	60.97	16.90	13.00	279.0	3.55	0.21	537
4.1	4	Es-4	Treatment	2.07	1.98	3.83	3.80	0.04	165.95	12.45	96.34	22.16	9.74	2.92	3.88	0.44	65.64	15.88	3.40	273.0	1.38	0.06	537
5	5	Es-5	Control	2.48	1.65	4.51	4.40	0.07	222.80	12.93	91.39	9.97	12.06	5.29	7.24	1.21	49.65	23.72	9.11	280.0	3.45	0.20	523
5.1	5	Es-5	Treatment	1.08	0.97	2.09	2.00	0.06	170.20	10.83	94.64	27.53	13.02	4.01	4.89	0.47	65.76	16.82	2.62	312.5	1.94	0.05	523
6	6	Es-6	Control	2.01	1.48	3.55	4.60	0.08	753.50	32.50	98.75	10.69	12.13	3.12	5.12	1.14	59.58	17.08	4.96	281.5	3.88	0.20	186
6.1	6	Es-6	Treatment	1.23	1.04	2.18	3.80	0.03	235.50	10.32	95.90	25.12	14.73	2.99	3.84	0.59	58.45	12.58	1.22	315.0	1.03	0.03	186
7	7	Es-7	Control	1.95	2.19	4.08	4.00	0.08	323.13	10.60	97.49	13.15	16.65	3.98	3.69	1.27	46.84	26.30	NA	NA	NA	NA	159
7.1	7	Es-7	Treatment	1.49	1.15	2.69	2.40	0.06	96.48	8.52	96.00	30.73	16.97	3.04	2.71	0.55	50.04	31.32	NA	NA	NA	NA	159
8	8	Es-8	Control	2.97	1.80	4.66	5.00	0.07	245.13	11.20	94.58	15.59	17.60	7.78	9.49	1.13	69.75	9.80	2.86	300.5	1.57	0.07	97
8.1	8	Es-8	Treatment	0.92	1.05	1.98	2.40	0.04	76.17	5.59	92.02	31.90	21.97	6.39	8.85	0.69	83.50	6.93	1.54	333.0	1.30	0.05	97
9	9	Es-9	Control	2.42	2.23	4.65	4.00	0.07	60.59	3.48	91.56	16.21	33.40	8.99	8.11	2.07	50.83	27.40	14.01	255.0	3.46	0.23	355
9.1	9	Es-9	Treatment	1.71	1.39	3.10	3.00	0.04	50.85	2.72	90.26	31.85	41.31	13.46	5.33	1.30	56.62	31.32	2.46	251.0	0.68	0.03	355
10	10	Es-10	Control	1.38	1.61	3.04	5.00	0.08	435.07	16.00	96.41	9.82	12.51	4.69	3.44	1.30	55.62	18.52	8.80	281.5	3.48	0.17	840
10.1	10	Es-10	Treatment	1.06	0.75	1.89	2.20	0.07	177.52	7.18	94.73	35.98	16.71	8.52	2.91	0.46	56.00	13.00	2.51	258.5	1.23	0.06	840
11	11	Es-11	Control	2.06	1.89	3.99	4.40	0.06	205.72	7.28	94.66	15.82	18.40	9.79	4.41	1.17	54.23	14.32	3.29	290.0	1.65	0.07	355
11.1	11	Es-11	Treatment	1.27	1.13	2.33	3.60	0.05	144.61	6.43	95.90	18.54	20.71	11.00	4.25	1.12	54.21	9.35	1.16	329.0	0.63	0.03	355
12	12	Es-12	Control	1.78	1.66	3.44	4.40	0.06	247.10	10.80	94.75	16.64	14.60	4.02	6.07	0.88	57.91	15.67	9.07	255.5	3.14	0.15	178
12.1	12	Es-12	Treatment	1.37	1.22	2.36	3.00	0.02	193.27	9.27	95.20	20.31	11.98	7.14	5.51	0.59	59.02	14.37	3.31	281.5	1.84	0.06	178
13	13	Es-13	Control	2.73	2.50	5.34	5.00	0.07	449.30	10.90	96.10	10.56	15.25	5.45	4.46	1.44	47.15	15.00	14.80	294.0	3.64	0.20	140
13.1	13	Es-13	Treatment	1.97	1.44	3.41	4.00	0.04	176.71	9.17	94.59	27.91	15.77	8.00	3.15	0.57	47.84	9.08	2.50	223.5	0.68	0.03	140
14	14	Es-14	Control	2.29	2.41	4.79	5.60	0.08	426.00	14.00	97.35	12.85	13.03	4.43	4.19	1.02	49.72	24.88	8.83	294.5	2.58	0.20	190
14.1	14	Es-14	Treatment	1.23	1.49	2.80	3.20	0.03	165.56	8.61	94.23	35.57	18.91	10.12	3.73	0.53	55.17	17.52	3.18	274.5	1.25	0.05	190
15	15	Es-15	Control	2.07	2.42	4.38	4.75	0.05	250.90	11.80	95.26	15.84	12.65	4.25	4.53	0.80	59.46	16.55	5.16	315.5	1.93	0.10	169
15.1	15	Es-15	Treatment	0.35	0.97	1.64	1.60	0.01	36.60	3.60	91.67	21.56	16.92	5.76	3.12	0.78	62.24	9.63	1.37	215.5	0.26	0.01	169
16	16	Es-16	Control	2.42	2.31	4.65	4.60	0.07	77.00	3.67	90.82	10.68	13.63	5.49	3.87	1.30	49.17	21.22	NA	NA	NA	NA	268
16.1	16	Es-16	Treatment	0.69	0.45	1.23	1.33	0.01	33.03	3.01	88.90	22.88	19.37	12.14	2.83	0.84	52.06	12.67	NA	NA	NA	NA	268
17	17	Es-17	Control	2.44	2.23	4.69	5.00	0.08	210.20	10.40	94.25	17.88	17.13	7.26	6.26	0.96	60.17	15.18	1.14	340.0	1.06	0.05	207
17.1	17	Es-17	Treatment	0.60	0.45	1.23	1.40	0.02	36.28	3.00	90.90	47.06	26.70	22.98	4.59	0.58	63.78	9.22	0.76	315.0	0.37	0.03	207
18	18	Es-18	Control	3.05	2.90	5.87	5.20	0.07	307.93	8.67	96.56	24.72	22.81	4.89	5.05	0.93	62.19	9.62	2.84	306.0	1.40	0.07	162
18.1	18	Es-18	Treatment	0.60	0.46	1.16	2.00	0.01	41.04	2.80	88.61	38.07	25.59	13.62	3.84	0.68	68.11	12.50	1.43	349.5	1.00	0.03	162
19	19	Es-19	Control	2.25	2.18	4.43	3.40	0.04	104.92	5.34	92.96	12.51	18.37	2.61	5.99	1.47	57.77	18.75	2.16	287.0	3.64	0.20	186
19.1	19	Es-19	Treatment	1.23	1.49	2.80	3.20	0.03	70.57	4.08	94.69	32.73	22.20	6.84	3.78	0.68	59.43	16.52	1.62	293.0	0.56	0.04	186
20	20	Es-20	Control	1.99	2.34	4.33	3.00	0.04	42.25	3.10	88.38	13.68	26.53	13.48	6.81	1.94	62.26	22.22	12.70	238.0	3.00	0.16	186
20.1	20	Es-20	Treatment	0.35	0.97	2.80	1.60	0.01	30.15	2.60	91.38	48.28	33.45	21.62	4.74	0.72	65.09	14.67	1.75	215.5	0.46	0.02	186
21	21	Es-21	Control	2.03	2.68	4.72	3.20	0.04	90.56	4.18	94.99	24.90	24.81	8.28	10.54	1.00	46.49	9.95	12.18	274.0	4.14	0.25	171
21.1	21	Es-21	Treatment	0.60	0.46	1.78	2.00	0.02	61.55	3.25	95.42	44.92	30.38	12.05	5.82	0.68	48.78	16.90	2.58	265.5	1.23	0.06	171
22	22	Es-22	Control	1.99	2.47	4.33	3.40	0.04	48.30	2.02	96.41	16.72	12.71	9.38	5.53	0.76	59.96	18.98	4.26	259.0	1.32	0.06	170
22.1	22	Es-22	Treatment	1.22	0.78	2.13	2.00	0.04	35.92	1.32	95.93	28.21	15.63	13.33	4.66	0.56	71.26	8.02	1.24	275.0	0.82	0.03	170
23	23	Es-23	Control	1.73	1.80	3.53	3.00	0.05	138.11	4.17	94.44	17.41	10.94	8.64	3.17	0.63	63.71	21.50	5.04	246.0	1.56	0.06	210
23.1	23	Es-23	Treatment	0.63	0.53	1.17	2.00	0.03	37.66	3.38	90.88	26.89	16.58	9.95	2.30	0.62	66.40	8.83	0.78	264.0	0.66	0.03	210
24	24	Es-24	Control	1.40	1.41	2.84	3.00	0.04	107.80	7.67	90.98	13.83	13.91	6.39	14.25	1.01	61.26	14.22	4.47	276.5	1.52	0.06	209
24.1	24	Es-24	Treatment	0.75	0.49	1.26	1.60	0.03	66.41	3.15	89.62	28.33	19.10	7.40	5.60	0.68	67.51	9.42	0.90	361.0	0.61	0.03	209
25	25	Es-25	Control	2.39	2.74	5.18	3.00	0.04	95.34	5.29	93.99	15.45	13.94	5.81	11.36	0.91	50.58	19.83	2.27	266.0	0.99	0.06	151
25.1	25	Es-25	Treatment	1.17	0.64	1.84	2.00	0.04	39.93	4.31	90.57	41.97	26.23	11.76	9.95	0.62	48.61	12.95	0.71	350.5	0.38	0.03	151

2. Initial statistical analysis. Normality test and significant difference test.

2.1 Morphological traits

There is a statistical difference between control and treatment for all morphological traits

• Shoot length (SL): Shoot length is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for SL in salt-stressed plants is 1.10 ± 0.48 and 2.30 ± 0.56 for control plants. SL stress index of accessions varied from 16.73 to 89.12

• Root length (RL): Root length is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for RL in salt-stressed plants is 0.98 ± 0.43 and 2.17 ± 0.42 for control plants. RL stress index of accessions varied from 15.91 to 73.53

• Plant height (PH): Plant height is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for PH in salt-stressed plants is 2.18 ± 0.76 and 4.43 ± 0.77 for control plants. PH stress index of accessions varied from 19.83 to 74.07

• Number of leaves(NL): Number of leaves is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for NL in salt-stressed plants is 2.43 ± 0.82 and 4.22 ± 0.78 for control plants. NL stress index of accessions varied from 28.00 to 94.12

• Leaf area (LA): Leaf area is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for LA in salt-stressed plants is 0.03 ± 0.02 and 0.07 ± 0.03 for control plants. LA stress index of accessions varied from 75.95 to 100

#Performing Shapiro–Wilk test of normality
spm <- numeric()
nor_spm <- c()
for(i in names(Morpho_t[4:8])) { 
  an1 <- aov(Morpho_t[, i] ~ Morpho_t$Treat_Contr)
  spm[i] <- shapiro.test(an1$residuals)$p.value
  nor_spm <- c(nor_spm, ifelse (spm[i] <= 0.05, "False", "True"))
}
#Creating summary table with the results
spm_table <- data.frame ("P-value" = spm, "Normality" = nor_spm)

#Creating the caption for the summary table
knitr::kable(spm_table, caption = "Table 2.1 Shapiro–Wilk test results for morphological traits", align="c")

Table 2.1 Shapiro–Wilk test results for morphological traits

	P.value	Normality
Shoot_Length	0.0000307	False
Root_Length	0.0187623	False
Plant_Height	0.0315918	False
Number_Leaves	0.0000000	False
Leaf_Area	0.0000000	False

#Performing significance test and plotting the results and values distribution
for (i in (4:8)) {
  bmorpho <- ggboxplot(Morpho_t,
                       x = "Treat_Contr",
                       y = names(Morpho_t[i]),
                       color = names(Morpho_t[3]),
                       palette = c("blue", "red"),
                       add = "jitter") +
    stat_compare_means(data = Morpho_t, method = "kruskal.test",
                       label.x.npc = "center",
                       label.y.npc = "top") +
    theme(legend.position = "None") +
    labs(x = "",
         caption = paste("Figure 2.", (i-3),
                         "Distribution boxplot of the mean values of the",
                         names(Morpho_t[i])))
  print(bmorpho)
}

2.2 Biomass traits

• Fresh weight (FW): Fresh weight is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for FW in salt-stressed plants 96.22 ± 66.00 and 241.06 ± 164.75 for control plants. FW stress index of accessions varied from 10.20 to 83.93

• Dry weight (DW): Dry weight is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for DW in salt-stressed plants is 6.26 ± 4.00 and 10.58 ± 6.89 for control plants. DW stress index of accessions varied from 24.67 to 88.26

#Performing Shapiro–Wilk test of normality
spb <- numeric()
nor_spb <- c()
for(i in names(Weight_ion[4:5])) { 
  an2 <- aov(Weight_ion[, i] ~ Weight_ion$Treat_Contr)
  spb[i] <- shapiro.test(an2$residuals)$p.value
  nor_spb <- c(nor_spb, ifelse (spb[i] <= 0.05, "False", "True"))
}

#Creating summary table with the results
spb_table <- data.frame ("P-value" = spb, "Normality" = nor_spb)

#Creating the caption for the summary table
knitr::kable(spb_table, caption = "Table 2.2 Shapiro–Wilk test results for biomass traits", align="c")

Table 2.2 Shapiro–Wilk test results for biomass traits

	P.value	Normality
Fresh_Weight	3e-07	False
Dry_Weight	7e-07	False

#Performing significance test and plotting the results and values distribution
for (i in (4:5)) {
  bwei <- ggboxplot(Weight_ion, 
                 x = "Treat_Contr",
                 y = names(Weight_ion[i]), 
                 color = names(Weight_ion[3]),
                 palette = c("blue", "red"),
                 add = "jitter") + 
    stat_compare_means(data = Weight_ion, method = "kruskal.test",
                       label.x.npc = "center",
                       label.y.npc = "top") +
    theme(legend.position = "None") +
    labs(x = "",
         caption = paste("Figure 2.", (i+2),
                         "Distribution boxplot of the mean values of the",
                         names(Weight_ion[i])))
  print(bwei)
} 

2.3 Relative water content (RWC), electrolyte leakage (EL) and chlorophyll content (SPAD or CC)

• Relative water content (RWC): Relative water content is not significantly different in the salt-stressed plants compared with the control (p > 0.05). The mean value for RWC in salt-stressed plants 92.77 X ± 2.65 and 92.10 ± 7.00 for control plants.

• Electrolyte leakage (EL): Electrolyte leakage is significantly increased in the salt-stressed plants compared with the control (p < 0.05). The mean value for EL in salt-stressed plants is 60.60 ± 9.32 and 56.51 ± 6.48 for control plants.

• Chlorophyll content (SPAD or CC): Chlorophyll content is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for CC in salt-stressed plants is 14.06 ± 6.02 and 18.17 ± 4.79 for control plants.

#Performing Shapiro–Wilk test of normality on the RWC and EL
spe <- numeric()
nor_spe <- c()
for(i in names(Weight_ion[c(6,12)])) { 
  an3 <- aov(Weight_ion[, i] ~ Weight_ion$Treat_Contr)
  spe[i] <- shapiro.test(an3$residuals)$p.value
  nor_spe <- c(nor_spe, ifelse (spe[i] <= 0.05, "False", "True"))
}

#Creating summary table with the results for RWC and EL
spe_table <- data.frame ("P-value" = spe, "Normality" = nor_spe)

#Performing Shapiro–Wilk test of normality on the SPAD
nor_spc <- c()
an4 <- aov(Chloro_c$Chlorophyll_Content ~ Chloro_c$Treat_Contr)
spc <- shapiro.test(an4$residuals)$p.value

#Creating summary table with the results for SPAD
nor_spc <- c(nor_spc, ifelse (spc <= 0.05, "False", "True"))
spc_table <- data.frame ("P-value" = spc, "Normality" = nor_spc)
row.names(spc_table) <- "Chlorophyll_Content"

#Uniting the summary tables with the results for RWC+EL and SPAD
spec_table <- dplyr::bind_rows(spe_table, spc_table)

#Creating the caption for the summary table
knitr::kable(spec_table, caption = "Table 2.3 Shapiro–Wilk test results for RWC, EL and SPAD", align="c")

Table 2.3 Shapiro–Wilk test results for RWC, EL and SPAD

	P.value	Normality
RWC	0.0000000	False
Electrolyte_Leakage	0.0009774	False
Chlorophyll_Content	0.0000000	False

#for RWC and EL
for (i in (c(6,12))) {
  bwei2 <- ggboxplot(Weight_ion, 
                 x = "Treat_Contr",
                 y = names(Weight_ion[i]), 
                 color = names(Weight_ion[3]),
                 palette = c("blue", "red"),
                 add = "jitter") + 
    stat_compare_means(data = Weight_ion, method = "kruskal.test",
                       label.x.npc = "center",
                       label.y.npc = "top") +
    theme(legend.position = "None") +
    labs(x = "",
         caption = paste("Figure 2.", ifelse((i<7), paste(i+2), paste(i-3)),
                         "Distribution boxplot of the mean values of the",
                         names(Weight_ion[i])))
  print(bwei2)
} 

#for SPAD
bchlo <- ggboxplot(Chloro_c, 
                 x = "Treat_Contr",
                 y = "Chlorophyll_Content", 
                 color = names(Chloro_c[3]),
                 palette = c("blue", "red"),
                 add = "jitter") 
bchlo + stat_compare_means(data = Chloro_c, method = "kruskal.test", 
                           label.x.npc = "center",
                           label.y.npc = "top") +
  theme(legend.position = "None") +
  labs(x = "", caption = paste("Fifure 2. 10 Distribution boxplot of the mean values of the",
                       names(Chloro_c[4])))

2.4 Mineral ion content

• Sodium (Na): Sodium is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for Na in salt-stressed plants is 30.03 ± 8.84 and 13.60 ± 5.00 for control plants.

• Potassium (K): Potassium is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for K in salt-stressed plants is 19.72 ± 7.48 and 15.48 ± 6.09 for control plants.

• Calcium (Ca): Calcium is significantly increased in the salt-stressed plants compared with the control (p < 0.05). The mean value for Ca in salt-stressed plants is 9.12 ± 5.32 and 6.03 ± 2.60 for control plants.

• Magnesium (Mg): Magnesium is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for Mg in salt-stressed plants is 4.37 ± 1.83 and 5.95 ± 2.85 for control plants.

• K/Na ratio: K/Na ratio is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for K/Na ratio in salt-stressed plants is 0.67 ± 0.20 and 1.20 ± 0.36 for control plants.

#Performing Shapiro–Wilk test of normality
spw <- numeric()
nor_spw <- c()
for(i in names(Weight_ion[7:11])) { 
  an5 <- aov(Weight_ion[, i] ~ Weight_ion$Treat_Contr)
  spw[i] <- shapiro.test(an5$residuals)$p.value
  nor_spw <- c(nor_spw, ifelse (spw[i] <= 0.05, "False", "True"))
}

#Creating summary table with the results
spw_table <- data.frame ("P-value" = spw, "Normality" = nor_spw)

#Creating the caption for the summary table
knitr::kable(spw_table, caption = "Table 2.4 Shapiro–Wilk test results for mineral ion content ", align="c")

Table 2.4 Shapiro–Wilk test results for mineral ion content

	P.value	Normality
Na	0.0002216	False
K	0.0000000	False
Ca	0.0000007	False
Mg	0.0000000	False
K_Na	0.0000002	False

#Performing significance test and plotting the results and values distribution
for (i in (7:11)) {
  bwei3 <- ggboxplot(Weight_ion, 
                 x = "Treat_Contr",
                 y = names(Weight_ion[i]), 
                 color = names(Weight_ion[3]),
                 palette = c("blue", "red"),
                 add = "jitter") + 
    stat_compare_means(data = Weight_ion, method = "kruskal.test",
                       label.x.npc = "center",
                       label.y.npc = "top") +
    theme(legend.position = "None") +
    labs(x = "",
         caption = paste("Figure 2.", (i+4),
                         "Distribution boxplot of the mean values of the",
                         names(Weight_ion[i])))
  print(bwei3)
}

2.5 Gas exchange attributes

• Photosynthesis rate (PR): Photosynthesus rate is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for PR in salt-stressed plants is 1.95 ± 1.02 and 7.53 ± 4.84 for control plants.

• Intercellular CO2 (ICO2): Intercellular CO2 is not significantly different in the salt-stressed plants compared with the control (p < 0.05). The mean value for ICO2 in salt-stressed plants is 284.70 ± 42.57 and 281.20 ± 22.90 for control plants.

• Transpiration rate (TR): Transpiration rate is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for TR in salt-stressed plants is 0.94 ± 0.48 and 2.57 ± 1.13 for control plants.

• Stomatal conductance (SC): Stomatal conductance is significantly reduced in the salt-stressed plants compared with the control (p < 0.05). The mean value for SC in salt-stressed plants is 0.039 ± 0.02 and 0.15 ± 0.08 for control plants.

#Performing Shapiro–Wilk test of normality
spg <- numeric()
nor_spg <- c()
for (i in names(Gas_e[c(4:7)])) { 
  an6 <- aov(Gas_e[, i] ~ Gas_e$Treat_Contr)
  spg[i] <- shapiro.test(an6$residuals)$p.value
  nor_spg <- c(nor_spg, ifelse (spg[i] <= 0.05, "False", "True"))
}

#Creating summary table with the results
spg_table <- data.frame ("P-value" = spg, "Normality" = nor_spg)

#Creating the caption for the summary table
knitr::kable(spg_table, caption = "Table 2.5 Shapiro–Wilk test results for gas exchange attributes", align="c")

Table 2.5 Shapiro–Wilk test results for gas exchange attributes

	P.value	Normality
Photsynthesis_Rate	0.0000139	False
Intercellular_CO2	0.8962970	True
Transpiration_Rate	0.0384559	False
Stomatal_Conductance	0.0000015	False

#Performing significance test and plotting the results and values distribution
#for intercellular CO2 (with ANOVA results, as this is the only normally distributed value)
Gas_e_withoutNA <- na.omit(Gas_e)
banova <- ggboxplot(Gas_e_withoutNA,
          x="Treat_Contr", 
          y= "Intercellular_CO2",
          color = names(Gas_e_withoutNA[3]),
          palette = c("blue", "red"),
          add = "jitter") 
banova  +   stat_compare_means(method = "anova") +
  theme(legend.position = "None") +
  labs(x = "",
       caption = paste("Figure 2. 16 Distribution boxplot of the mean values of the",
                       names(Gas_e_withoutNA[5])))

#for photosynthesis rate, transpiration rate, stomatal conductance
#(with Kruskal-Wallis results)
for (i in c(4,6:7)) {
  bgas <- ggboxplot(Gas_e_withoutNA, 
                 x = "Treat_Contr",
                 y = names(Gas_e_withoutNA[i]), 
                 color = names(Gas_e_withoutNA[3]),
                 palette = c("blue", "red"),
                 add = "jitter") + 
    stat_compare_means(data = Gas_e_withoutNA, method = "kruskal.test",
                       label.x.npc = "center",
                       label.y.npc = "top") +
    theme(legend.position = "None") +
    labs(x = "",
         caption = paste("Figure 2.", ifelse((i<5), paste(i+13), paste(i+12)),
                         "Distribution boxplot of the mean values of the",
                         names(Gas_e_withoutNA[i])))
  print(bgas)
}

x <- data.frame(dplyr::filter(table, table$Treat_Contr=="Treatment"))
x <- x[-(1:3)]
y <- data.frame(dplyr::filter(table, table$Treat_Contr=="Control"))
y <- y[-(1:3)]

Mean_Treat <- x %>% 
  summarise(Mean_SL = mean(Shoot_Length),
            Mean_SL = mean(Root_Length), 
            Mean_PH = mean(Plant_Height), 
            Mean_NL = mean(Number_Leaves), 
            Mean_LA = mean(Leaf_Area),
            Mean_FW = mean(Fresh_Weight),
            Mean_DW = mean(Dry_Weight),
            Mean_RWC = mean(RWC),
            Mean_Na = mean(Na),
            Mean_K = mean(K),
            Mean_Ca = mean(Ca),
            Mean_Mg = mean(Mg),
            Mean_K_Na = mean(K_Na),
            Mean_EL = mean(Electrolyte_Leakage),
            Mean_CC = mean(Chlorophyll_Content),
            Mean_PR = mean (Photsynthesis_Rate, na.rm=T),
            Mean_ICO2 = mean(Intercellular_CO2, na.rm=T),
            Mean_TR = mean(Transpiration_Rate, na.rm=T),
            Mean_S = mean(Stomatal_Conductance, na.rm=T)) 
Mean_Control <- y %>% 
  summarise(Mean_SL = mean(Shoot_Length),
            Mean_SL = mean(Root_Length), 
            Mean_PH = mean(Plant_Height), 
            Mean_NL = mean(Number_Leaves), 
            Mean_LA = mean(Leaf_Area),
            Mean_FW = mean(Fresh_Weight),
            Mean_DW = mean(Dry_Weight),
            Mean_RWC = mean(RWC),
            Mean_Na = mean(Na),
            Mean_K = mean(K),
            Mean_Ca = mean(Ca),
            Mean_Mg = mean(Mg),
            Mean_K_Na = mean(K_Na),
            Mean_EL = mean(Electrolyte_Leakage),
            Mean_CC = mean(Chlorophyll_Content),
            Mean_PR = mean (Photsynthesis_Rate, na.rm=T),
            Mean_ICO2 = mean(Intercellular_CO2, na.rm=T),
            Mean_TR = mean(Transpiration_Rate, na.rm=T),
            Mean_S = mean(Stomatal_Conductance, na.rm=T)) 
Treatment <- c('Treatment','Control')
Means <- rbind(Mean_Treat, Mean_Control)
Means$Treat_Contr <- Treatment

Sd_Treatment <- x %>%
    summarise(SD_SL = sd(Shoot_Length),
            SD_RL = sd(Root_Length),
            SD_PH = sd(Plant_Height),
            SD_NL = sd(Number_Leaves),
            SD_LA = sd(Leaf_Area),
            SD_FW = sd(Fresh_Weight),
            SD_DW = sd(Dry_Weight),
            SD_RWC = sd(RWC),
            SD_Na = sd(Na),
            SD_K = sd(K),
            SD_Ca = sd(Ca),
            SD_Mg = sd(Mg),
            SD_K_NA = sd(K_Na),
            SD_EL = sd(Electrolyte_Leakage),
            SD_CC = sd(Chlorophyll_Content),
            SD_PR = sd (Photsynthesis_Rate, na.rm=T),
            SD_I_CO2 = sd(Intercellular_CO2, na.rm=T),
            SD_TR = sd(Transpiration_Rate, na.rm=T),
            SD_SC = sd(Stomatal_Conductance, na.rm=T))
Sd_Control <-   y %>%
  summarise(SD_SL = sd(Shoot_Length),
            SD_RL = sd(Root_Length),
            SD_PH = sd(Plant_Height),
            SD_NL = sd(Number_Leaves),
            SD_LA = sd(Leaf_Area),
            SD_FW = sd(Fresh_Weight),
            SD_DW = sd(Dry_Weight),
            SD_RWC = sd(RWC),
            SD_Na = sd(Na),
            SD_K = sd(K),
            SD_Ca = sd(Ca),
            SD_Mg = sd(Mg),
            SD_K_NA = sd(K_Na),
            SD_EL = sd(Electrolyte_Leakage),
            SD_CC = sd(Chlorophyll_Content),
            SD_PR = sd (Photsynthesis_Rate, na.rm=T),
            SD_I_CO2 = sd(Intercellular_CO2, na.rm=T),
            SD_TR = sd(Transpiration_Rate, na.rm=T),
            SD_SC = sd(Stomatal_Conductance, na.rm=T))
Treatment <- c('Treatment','Control')
SD <- rbind(Sd_Treatment,Sd_Control)
SD$Treat_Contr <- Treatment

knitr::kable(Means, caption = "Table 7. Summary table for the mean of treated and control plants")

Table 7. Summary table for the mean of treated and control plants

Mean_SL	Mean_PH	Mean_NL	Mean_LA	Mean_FW	Mean_DW	Mean_RWC	Mean_Na	Mean_K	Mean_Ca	Mean_Mg	Mean_K_Na	Mean_EL	Mean_CC	Mean_PR	Mean_ICO2	Mean_TR	Mean_S	Treat_Contr
0.98288	2.17992	2.43456	0.03304	96.22133	6.262533	92.76587	30.03500	19.71913	9.120667	4.368333	0.6673333	60.60373	14.06147	1.947391	284.6957	0.9404348	0.0386957	Treatment
2.17392	4.43336	4.22200	0.06664	241.06840	10.582267	92.09667	13.60367	15.48453	6.027733	5.948000	1.1988667	56.51267	18.17267	7.526739	281.1957	2.5736957	0.1458696	Control

knitr::kable(SD, caption = "Table 8. Summary table for the standard deviation (SD) of treated and control plants")

Table 8. Summary table for the standard deviation (SD) of treated and control plants

SD_SL	SD_RL	SD_PH	SD_NL	SD_LA	SD_FW	SD_DW	SD_RWC	SD_Na	SD_K	SD_Ca	SD_Mg	SD_K_NA	SD_EL	SD_CC	SD_PR	SD_I_CO2	SD_TR	SD_SC	Treat_Contr
0.4802925	0.4291682	0.7607550	0.823898	0.0156644	66.00101	3.997402	2.650396	8.841386	7.478913	5.315845	1.827305	0.1959651	9.321449	6.022014	1.017193	42.57340	0.475834	0.0179150	Treatment
0.5639478	0.4196222	0.7721049	0.780235	0.0254147	164.75151	6.887152	6.997688	5.007557	6.091253	2.606707	2.854427	0.3648619	6.479804	4.789543	4.839646	22.89638	1.131070	0.0801618	Control

(3) Pearson correlation

# table with all the variables but without 3 first columns
all_data <- na.omit(table[4:23])

res1 <- cor.mtest(all_data, conf.level = .95)
M1 <-cor(all_data)
corrplot(M1, p.mat = res1$p, sig.level = .05, type = "lower")

Figure 3.1. Pearson correlation table for all variables. X represent non-significative correlation (p-value > 0.05)

(4) Salt tolerance index (STI)

x <- data.frame(dplyr::filter(table, table$Treat_Contr=="Treatment"))
x <- x[-(1:3)]
y <- data.frame(dplyr::filter(table, table$Treat_Contr=="Control"))
y <- y[-(1:3)]


#salt tolerance index for morphological traits
STI_Shoot_Length <- x[1]/y[1]
STI_Root_Length <- x[2]/y[2]
STI_Plant_Height <- x[3]/y[3]
STI_Number_Leaves <- x[4]/y[4]
STI_Leaf_Area <- x[5]/y[5]

#trying to make a for loop
#STI1 <- data.frame()
#vecteur <- c()
#for (i in names(x)){
#  for (j in 1:4){
#    name <- paste("STI", i ,sep = "_" )
#    name <- x[j]/x[j]
#    vecteur <- c(vecteur, name)}}

#salt tolerance index for biomass
STI_Fresh_Weight <- x[6]/y[6]
STI_Dry_Weight <- x[7]/y[7]

#salt tolerance index for RWC, EL and Chlorophyll content
STI_Relative_water_content  <- x[8]/y[8]
names(STI_Relative_water_content)[1] <- "Relative_water_content" # change the name of the column of the data
STI_Electrolyte_Leakage  <- x[14]/y[14]
STI_Chlorophyll_Content <- x[15]/y[15]

# salt tolerance index for Mineral ion content
STI_Na <- x[9]/y[9]
STI_K <- x[10]/y[10]
STI_Ca <- x[11]/y[11]
STI_Mg <- x[12]/y[12]
STI_K_Na <- x[13]/y[13]

# salt tolerance index for Gas exchange attributes
STI_Photosynthesis_rate <- x[16]/y[16]
STI_Intercellular_CO2 <- x[17]/y[17]
STI_Transpiration_Rate <- x[18]/y[18]
STI_Stomatal_Conductance <- x[19]/y[19]

STI <- data.frame(STI_Shoot_Length,
                  STI_Root_Length, 
                  STI_Plant_Height, 
                  STI_Number_Leaves, 
                  STI_Leaf_Area, 
                  STI_Fresh_Weight, 
                  STI_Dry_Weight,
                  STI_Relative_water_content, 
                  STI_Electrolyte_Leakage,
                  STI_Na,
                  STI_K,
                  STI_Ca,
                  STI_Mg,
                  STI_K_Na,
                  STI_Chlorophyll_Content,
                  STI_Photosynthesis_rate,
                  STI_Intercellular_CO2,
                  STI_Transpiration_Rate,
                  STI_Stomatal_Conductance)

knitr::kable(STI, caption = "Table 4.1. Summary table for salt tolerance index")

Table 4.1. Summary table for salt tolerance index

	Shoot_Length	Root_Length	Plant_Height	Number_Leaves	Leaf_Area	Fresh_Weight	Dry_Weight	Relative_water_content	Electrolyte_Leakage	Na	K	Ca	Mg	K_Na	Chlorophyll_Content	Photsynthesis_Rate	Intercellular_CO2	Transpiration_Rate	Stomatal_Conductance
1.1	0.8912355	0.6397671	0.7407407	0.6190476	0.6956522	0.5012338	0.8625796	1.1842036	0.9481757	4.673913	1.3245413	0.8746888	0.9281364	0.2824561	0.8084896	0.3725332	0.8583916	0.5174051	0.3563218
2.1	0.5455830	0.6594203	0.5068109	0.6363636	0.5000000	0.3398344	0.7523677	1.2812980	1.0102382	3.953065	1.4744608	0.8739300	0.8126195	0.3735763	0.7181102	0.6496350	1.0626151	1.2375691	1.0000000
3.1	0.1974957	0.1920136	0.2569204	0.3027273	0.0759494	0.1019714	0.2466527	1.1603017	1.2178019	2.284498	1.5078206	1.0076112	0.9375000	0.6530612	0.7071429	0.0788235	0.8881239	0.1327751	0.0720000
4.1	0.5805727	0.7180233	0.6326396	0.8260870	0.4375000	0.4774903	0.6312320	1.0031933	1.0765362	3.128941	1.4041326	1.0880893	0.7604439	0.4489796	0.9398422	0.2615385	0.9784946	0.3887324	0.2941176
5.1	0.4346774	0.5895884	0.4631766	0.4545455	0.8484848	0.7639138	0.8376289	1.0355254	1.3243824	2.762542	1.0798784	0.7580340	0.6751035	0.3922652	0.7092059	0.2883031	1.1160714	0.5616836	0.2592593
6.1	0.6141575	0.7054054	0.6148649	0.8260870	0.3750000	0.3125481	0.3173846	0.9711378	0.9810355	2.349392	1.2147568	0.9583333	0.7517107	0.5198238	0.7365854	0.2469758	1.1190053	0.2654639	0.1604938
7.1	0.7641026	0.5242452	0.6591577	0.6000000	0.7368421	0.2985661	0.8040881	0.9847842	1.0683937	2.336629	1.0196235	0.7644593	0.7346570	0.4330709	1.1907478	NA	NA	NA	NA
8.1	0.3086253	0.5849057	0.4244635	0.4800000	0.6285714	0.3107289	0.4988095	0.9729330	1.1970276	2.046184	1.2483425	0.8205567	0.9322093	0.6106195	0.7074830	0.5376532	1.1081531	0.8280255	0.7777778
9.1	0.7061258	0.6245520	0.6665232	0.7500000	0.6000000	0.8392562	0.7816092	0.9858376	1.1139822	1.964433	1.2366031	1.4981454	0.6565325	0.6274194	1.1429440	0.1755889	0.9843137	0.1965318	0.1428571
10.1	0.7718023	0.4665012	0.6230263	0.4400000	0.8536585	0.4080294	0.4489583	0.9825059	1.0069524	3.665535	1.3353756	1.8146307	0.8441433	0.3564103	0.7020702	0.2857955	0.9182948	0.3548851	0.3623188
11.1	0.6143411	0.5955649	0.5832497	0.8181818	0.8214286	0.7029466	0.8826356	1.0131178	0.9996619	1.171353	1.1260306	1.1241696	0.9644747	0.9585122	0.6530850	0.3531202	1.1344828	0.3829787	0.3703704
12.1	0.7705287	0.7352587	0.6868100	0.6818182	0.4000000	0.7821395	0.8580247	1.0048024	1.0193132	1.220186	0.8205479	1.7769486	0.9071938	0.6742857	0.9170213	0.3651407	1.1017613	0.5875796	0.4262295
13.1	0.7205560	0.5748599	0.6379633	0.8000000	0.5945946	0.3933007	0.8412844	0.9843384	1.0147048	2.641426	1.0343169	1.4672783	0.7078189	0.3921569	0.6055556	0.1689189	0.7602041	0.1854396	0.1234568
14.1	0.5388646	0.6171096	0.5854576	0.5714286	0.3571429	0.3886307	0.6150000	0.9679176	1.1095468	2.769071	1.4512663	2.2868976	0.8902148	0.5228758	0.7039518	0.3601359	0.9320883	0.4835590	0.2592593
15.1	0.1673114	0.4004955	0.3745438	0.3368421	0.1851852	0.1458749	0.3050847	0.9623137	1.0466700	1.361111	1.3379447	1.3552941	0.6887417	0.9751553	0.5820745	0.2655039	0.6830428	0.1347150	0.0952381
16.1	0.2872517	0.1958406	0.2637694	0.2895652	0.1714286	0.4290074	0.8201635	0.9788952	1.0586022	2.142991	1.4204156	2.2103825	0.7299742	0.6483376	0.5970149	NA	NA	NA	NA
17.1	0.2446634	0.2021563	0.2612958	0.2800000	0.2894737	0.1725975	0.2884615	0.9644903	1.0599136	2.632761	1.5583658	3.1660542	0.7332268	0.6041667	0.6070252	0.6666667	0.9264706	0.3474178	0.5263158
18.1	0.1956664	0.1590909	0.1982964	0.3846154	0.1621622	0.1332756	0.3225000	0.9176678	1.0951118	1.540048	1.1216572	2.7861528	0.7603960	0.7338129	1.2998267	0.5008787	1.1421569	0.7168459	0.3703704
19.1	0.5484444	0.6816514	0.6327913	0.9411765	0.7142857	0.6726554	0.7635853	1.0186102	1.0286753	2.615610	1.2084392	2.6221228	0.6307008	0.4625850	0.8808889	0.7505774	1.0209059	0.1552198	0.1851852
20.1	0.1738693	0.4141759	0.6471132	0.5333333	0.3157895	0.7136095	0.8387097	1.0340028	1.0453706	3.529240	1.2608368	1.6038576	0.6960352	0.3685567	0.6601650	0.1377953	0.9054622	0.1533333	0.1230769
21.1	0.2930187	0.1722595	0.3778626	0.6250000	0.4500000	0.6796849	0.7768924	1.0045268	1.0491827	1.803975	1.2245062	1.4547284	0.5526565	0.6750000	1.6984925	0.2113254	0.9689781	0.2971014	0.2323232
22.1	0.6120603	0.3154907	0.4909931	0.5882353	0.8571429	0.7436083	0.6534979	0.9949697	1.1884034	1.687400	1.2295189	1.4203516	0.8420735	0.7324561	0.4223003	0.2919109	1.0617761	0.6226415	0.5217391
23.1	0.3644752	0.2955556	0.3299377	0.6666667	0.6400000	0.2726450	0.8100000	0.9623571	1.0422465	1.544219	1.5155393	1.1520648	0.7255521	0.9841270	0.4108527	0.1536174	1.0731707	0.4244373	0.4000000
24.1	0.5350501	0.3480114	0.4440535	0.5333333	0.8000000	0.6160173	0.4104348	0.9850878	1.1019697	2.048939	1.3736817	1.1574557	0.3929825	0.6721854	0.6623681	0.2011173	1.3056058	0.4013158	0.4347826
25.1	0.4878661	0.2348651	0.3546893	0.6666667	1.0000000	0.4188665	0.8152585	0.9636486	0.9610544	2.716135	1.8820856	2.0229358	0.8764309	0.6875000	0.6529412	0.3127753	1.3176692	0.3857868	0.4800000

(5) Membership function value (MFV)

#Membership function value 
# 1-results for the traits inversely related to salt tolerance: which are electrolyte leakage, Na, K and Ca
MFV <- STI %>%
  mutate(SL = (STI$Shoot_Length - min(STI$Shoot_Length))/(max(STI$Shoot_Length)- min(STI$Shoot_Length)),
         RL = (STI$Root_Length - min(STI$Root_Length))/(max(STI$Root_Length)- min(STI$Root_Length)),
         PH = (STI$Plant_Height - min(STI$Plant_Height))/(max(STI$Plant_Height)- min(STI$Plant_Height)),
         NL = (STI$Number_Leaves - min(STI$Number_Leaves))/(max(STI$Number_Leaves)- min(STI$Number_Leaves)),
         LA = (STI$Leaf_Area - min(STI$Leaf_Area))/(max(STI$Leaf_Area)- min(STI$Leaf_Area)),
         FW = (STI$Fresh_Weight - min(STI$Fresh_Weight))/(max(STI$Fresh_Weight)- min(STI$Fresh_Weight)) ,
         DW = (STI$Dry_Weight - min(STI$Dry_Weight))/(max(STI$Dry_Weight)- min(STI$Dry_Weight)),
         RWC = (STI$Relative_water_content - min(STI$Relative_water_content))/(max(STI$Relative_water_content)- min(STI$Relative_water_content)),
         EL = 1-(STI$Electrolyte_Leakage - min(STI$Electrolyte_Leakage))/(max(STI$Electrolyte_Leakage)- min(STI$Electrolyte_Leakage)),
         CC = (STI$Chlorophyll_Content - min(STI$Chlorophyll_Content))/(max(STI$Chlorophyll_Content)- min(STI$Chlorophyll_Content)),
         Na = 1-(STI$Na - min(STI$Na))/(max(STI$Na)- min(STI$Na)),
         K = 1-(STI$K - min(STI$K))/(max(STI$K)- min(STI$K)),
         Ca =1- (STI$Ca - min(STI$Ca))/(max(STI$Ca)- min(STI$Ca)),
         Mg=(STI$Mg - min(STI$Mg))/(max(STI$Mg)- min(STI$Mg)),
         K_Na= (STI$K_Na - min(STI$K_Na))/(max(STI$K_Na)- min(STI$K_Na)),
         PR= (STI$Photsynthesis_Rate - min(STI$Photsynthesis_Rate, na.rm=T))/(max(STI$Photsynthesis_Rate, na.rm=T)- min(STI$Photsynthesis_Rate, na.rm=T)),
         ICO2= (STI$Intercellular_CO2 - min(STI$Intercellular_CO2, na.rm=T))/(max(STI$Intercellular_CO2, na.rm=T)- min(STI$Intercellular_CO2, na.rm=T)),
         TR= (STI$Transpiration_Rate - min(STI$Transpiration_Rate, na.rm=T))/(max(STI$Transpiration_Rate, na.rm=T)- min(STI$Transpiration_Rate, na.rm=T)),
         SC= (STI$Stomatal_Conductance - min(STI$Stomatal_Conductance, na.rm=T))/(max(STI$Stomatal_Conductance, na.rm=T)- min(STI$Stomatal_Conductance, na.rm=T)))%>%
  select(SL, RL, PH, NL, LA, FW, DW, RWC, EL, CC, Na, K, Ca, Mg, K_Na, PR, ICO2, TR, SC)

knitr::kable(MFV, caption = "Table 5.1. MFV table based on STI", align="c")

Table 5.1. MFV table based on STI

SL	RL	PH	NL	LA	FW	DW	RWC	EL	CC	Na	K	Ca	Mg	K_Na	PR	ICO2	TR	SC
1.0000000	0.8342641	1.0000000	0.5127944	0.6706373	0.5415308	0.9684647	0.7329858	1.0000000	0.3088107	0.0000000	0.5252233	0.9515557	0.9364151	0.0000000	0.4372282	0.2763024	0.3481463	0.3063813
0.5225294	0.8683744	0.5687487	0.5389841	0.4589041	0.3226203	0.7951708	1.0000000	0.8350309	0.2386207	0.2058062	0.3839946	0.9518709	0.7342830	0.1298617	0.8497332	0.5981036	1.0000000	1.0000000
0.0416954	0.0571408	0.1080738	0.0343740	0.0000000	0.0000000	0.0000000	0.6672545	0.2833031	0.2301033	0.6821910	0.3525687	0.8963558	0.9527996	0.5281751	0.0000000	0.3231526	0.0000000	0.0000000
0.5708628	0.9700860	0.8007147	0.8259322	0.3912671	0.5093267	0.6047008	0.2351993	0.6588033	0.4108210	0.4410979	0.4502459	0.8629350	0.6429858	0.2373242	0.2719969	0.4655524	0.2316787	0.2393509
0.3693288	0.7471738	0.4883085	0.2639922	0.8360315	0.8978110	0.9292329	0.3241139	0.0000000	0.2317055	0.5457070	0.7557030	1.0000000	0.4936569	0.1564965	0.3118398	0.6823363	0.3882249	0.2017880
0.6172555	0.9481864	0.7679469	0.8259322	0.3236301	0.2856111	0.1112167	0.1470450	0.9126550	0.2529688	0.6636635	0.6286436	0.9168199	0.6277044	0.3382892	0.2503183	0.6869594	0.1201028	0.0953597
0.8243836	0.6337638	0.8496011	0.4839858	0.7152127	0.2666469	0.8764943	0.1845734	0.6804469	0.6056780	0.6673075	0.8124649	0.9973317	0.5978639	0.2146515	NA	NA	NA	NA
0.1952055	0.7390464	0.4169407	0.3024911	0.5980431	0.2831436	0.3964837	0.1519818	0.3385234	0.2303674	0.7502312	0.5970048	0.9740356	0.9435420	0.4676884	0.6830325	0.6698591	0.6293032	0.7605364
0.7442969	0.8078568	0.8631795	0.7108541	0.5671233	1.0000000	0.8411492	0.1874700	0.5592676	0.5685529	0.7735715	0.6080637	0.6926474	0.4611611	0.4916311	0.1440488	0.4747217	0.0577091	0.0763547
0.8350198	0.5335430	0.7829926	0.2419929	0.8416305	0.4151151	0.3180992	0.1783078	0.8437650	0.2261638	0.2878975	0.5150171	0.5612177	0.7894435	0.1053972	0.3081068	0.3706938	0.2010420	0.3128436
0.6175091	0.7575467	0.7096642	0.8139761	0.8067515	0.8151195	1.0000000	0.2624920	0.8631439	0.1881211	1.0000000	0.7122262	0.8479516	1.0000000	0.9634945	0.4083292	0.7113476	0.2264708	0.3215198
0.8332604	1.0000000	0.9005783	0.6077321	0.3506849	0.9225310	0.9613026	0.2396242	0.8109084	0.3930980	0.9860579	1.0000000	0.5768663	0.8997697	0.5584236	0.4262233	0.6597874	0.4116645	0.3817128
0.7642301	0.7216109	0.8105291	0.7864769	0.5612736	0.3951381	0.9349807	0.1833474	0.8231580	0.1512091	0.5802862	0.7986233	0.7054658	0.5509025	0.1563421	0.1341196	0.1215854	0.0476690	0.0554491
0.5132489	0.7949398	0.7137344	0.4407728	0.3043053	0.3888040	0.5791780	0.1381892	0.5710574	0.2276250	0.5438428	0.4058446	0.3650952	0.8700597	0.3426388	0.4187730	0.3924285	0.3175107	0.2017880
0.0000000	0.4189831	0.3249133	0.0859712	0.1182141	0.0595475	0.0918767	0.1227783	0.7381909	0.1329734	0.9458231	0.5125969	0.7519705	0.5175211	0.9872138	0.2778999	0.0000000	0.0017559	0.0250411
0.1656807	0.0637829	0.1206998	0.0144670	0.1033268	0.4435681	0.9017708	0.1683783	0.7064738	0.1445763	0.7225921	0.4349068	0.3968703	0.5896698	0.5214432	NA	NA	NA	NA
0.1068509	0.0747446	0.1161398	0.0000000	0.2310743	0.0957922	0.0657389	0.1287642	0.7029881	0.1523505	0.5827600	0.3049537	0.0000000	0.5953613	0.4584921	0.8750871	0.3835766	0.1942830	0.4895644
0.0391685	0.0000000	0.0000000	0.1582261	0.0932988	0.0424588	0.1192599	0.0000000	0.6094273	0.6903903	0.8947355	0.7163462	0.1577650	0.6429022	0.6432600	0.6282885	0.7234399	0.5286694	0.3215198
0.5264821	0.9069588	0.8009945	1.0000000	0.6908023	0.7740346	0.8128090	0.2775962	0.7860230	0.3650370	0.5876568	0.6345949	0.2258833	0.4159607	0.2567142	1.0000000	0.5323812	0.0203157	0.1219668
0.0090589	0.4427269	0.8273969	0.3831554	0.2595530	0.8295818	0.9309323	0.3199268	0.7416450	0.1936196	0.3268105	0.5852348	0.6487473	0.5302833	0.1227079	0.0877877	0.3504730	0.0186082	0.0550398
0.1736470	0.0228555	0.3310315	0.5217972	0.4047945	0.7835690	0.8337327	0.2388663	0.7315120	1.0000000	0.8193829	0.6194593	0.7106775	0.2793985	0.5594416	0.1972477	0.4505569	0.1487393	0.1727621
0.6143585	0.2714483	0.5395884	0.4661922	0.8454012	0.8702701	0.6397110	0.2125838	0.3614476	0.0088903	0.8526658	0.6147372	0.7249535	0.7858219	0.6413263	0.3172105	0.5967815	0.4434007	0.4846327
0.2723542	0.2368488	0.2426817	0.5848161	0.6104110	0.2314894	0.8857901	0.1228976	0.7499491	0.0000000	0.8935448	0.3452975	0.8363673	0.5819320	1.0000000	0.1113413	0.6147364	0.2639969	0.3534483
0.5079796	0.3278914	0.4530549	0.3831554	0.7835616	0.6972148	0.2575259	0.1854081	0.5911980	0.1953306	0.7494444	0.4789316	0.8341286	0.0000000	0.5554304	0.1820515	0.9809914	0.2430686	0.3909295
0.4428015	0.1315140	0.2883114	0.5848161	1.0000000	0.4298137	0.8940584	0.1264494	0.9657671	0.1880095	0.5589565	0.0000000	0.4747130	0.8459405	0.5772562	0.3482701	1.0000000	0.2290126	0.4396552

(6) Principal component analysis (PCA)

####------PCA on MFV-------###

#doing the PCA, adding the scaling into the function
MFV_salt.pca <- prcomp((na.omit(MFV[-c(8, 17, 20)])), scale. = TRUE)
#results of the PCA
MFV_salt.pca$sdev

##  [1] 2.1959249 1.6698449 1.3565852 1.2856275 1.2158618 1.1326328 0.9173147
##  [8] 0.8378035 0.7115879 0.6292612 0.4811136 0.4495871 0.3451879 0.2533067
## [15] 0.2128659 0.1474844 0.0741877

summary(MFV_salt.pca)

## Importance of components:
##                           PC1    PC2    PC3     PC4     PC5     PC6    PC7
## Standard deviation     2.1959 1.6698 1.3566 1.28563 1.21586 1.13263 0.9173
## Proportion of Variance 0.2837 0.1640 0.1082 0.09723 0.08696 0.07546 0.0495
## Cumulative Proportion  0.2837 0.4477 0.5559 0.65315 0.74011 0.81558 0.8651
##                            PC8     PC9    PC10    PC11    PC12    PC13    PC14
## Standard deviation     0.83780 0.71159 0.62926 0.48111 0.44959 0.34519 0.25331
## Proportion of Variance 0.04129 0.02979 0.02329 0.01362 0.01189 0.00701 0.00377
## Cumulative Proportion  0.90636 0.93615 0.95944 0.97306 0.98495 0.99196 0.99573
##                           PC15    PC16    PC17
## Standard deviation     0.21287 0.14748 0.07419
## Proportion of Variance 0.00267 0.00128 0.00032
## Cumulative Proportion  0.99840 0.99968 1.00000

#check, if the sums equal to quantity of observations (=17)
sum((MFV_salt.pca$sdev)^2)

## [1] 17

#plotting the results in order to evaluate the principal components
MFV_plot <- screeplot(MFV_salt.pca,
                      npcs = length(MFV_salt.pca$sdev),
                      type = "lines")

#plotting the results of the PCA
#making a scatterplot of PC1 and PC2
#creating data.frame for data labels in future plot
MFV_labels <- data.frame(Acc_loc$Elevation[-c(7, 16)])
row.names(MFV_labels) <- paste0("Ess-", c(1:6, 8:15, 17:25))
#creating the plot
MFV_pc12 <- autoplot(MFV_salt.pca, loadings = TRUE, loadings.colour = 'blue',
         loadings.label = TRUE, loadings.label.size = 5,                #plotting the variables
         data = MFV_labels, shape = FALSE, label.size = 3,              #plotting the accessions
         frame = TRUE)                                                  #plotting the frame (to see if there are any groups)
MFV_pc12 + ggtitle("Scatterplot of PC1 and PC2")                            #adding the title

#putting absolute values of the loading scores from PCA1 into a vector
MFV_loading_scores <- abs(MFV_salt.pca$rotation[,1])
#ranking the variables
MFV_var_scores_ranked <- sort(MFV_loading_scores, decreasing = TRUE)
# We used 8 highest ranked variables to include in the final MFV-based ranking
# of salt tolerance accessions
print(MFV_var_scores_ranked) # Result from PCA analysis (from PCA_analysis.R)

##           PH           SL           RL           NL           DW           FW 
## 0.4179220239 0.3843371322 0.3655251106 0.3440154296 0.3304446450 0.3118996483 
##           LA         K_Na           Na            K           Ca           EL 
## 0.2467265352 0.2306101451 0.1746912100 0.1736000724 0.1438780842 0.1395916180 
##           Mg           TR           SC           PR           CC 
## 0.0455479807 0.0433627723 0.0156462600 0.0100021446 0.0005731188

(7) MFV Ranking

MFV <- MFV %>%
  mutate(Mean = rowMeans(MFV[c(1:7, 15)], na.rm=T))%>%
  select(SL, RL, PH, NL, LA, FW, DW, RWC, EL, CC, Na, K, Ca, Mg, K_Na, PR, ICO2, TR, SC, Mean)

MFV_Ranked <- MFV[order(-MFV$Mean),]
MFV_Ranked <- MFV_Ranked[20]
MFV_Ranked$Category <- c("Highly tolerant",(rep("Tolerant", 4)), (rep("Moderately tolerant", 15)), (rep("Sensitive", 4)), "Highly sensitive" )

knitr::kable(MFV_Ranked, caption = "Table 7.1. Salt tolerance ranking based on MFV mean from 8 variables identified by PCA", align="c")

Table 7.1. Salt tolerance ranking based on MFV mean from 8 variables identified by PCA

	Mean	Category
11	0.8105077	Highly tolerant
12	0.7668141	Tolerant
9	0.7532614	Tolerant
19	0.7210994	Tolerant
1	0.6909614	Tolerant
13	0.6413227	Moderately tolerant
4	0.6137768	Moderately tolerant
22	0.6110370	Moderately tolerant
7	0.6080925	Moderately tolerant
5	0.5860469	Moderately tolerant
25	0.5435714	Moderately tolerant
6	0.5272585	Moderately tolerant
2	0.5256492	Moderately tolerant
14	0.5097027	Moderately tolerant
10	0.5092238	Moderately tolerant
23	0.5080489	Moderately tolerant
24	0.4957268	Moderately tolerant
20	0.4756391	Moderately tolerant
21	0.4538586	Moderately tolerant
8	0.4248803	Moderately tolerant
16	0.2918424	Sensitive
15	0.2608400	Sensitive
17	0.1436041	Sensitive
18	0.1369590	Sensitive
3	0.0961824	Highly sensitive

(8) Dendogram

#table with MFV from 8 important variables of PCA
important_var <- na.omit(MFV)
important_var <- MFV[c(1:7, 15)]

#make the dendogram
dend <- important_var %>% # change MFV_Ranked with important_var in function of professor answer 
  scale %>% 
  dist(method = "euclidean") %>% 
  hclust(method = "ward.D2") %>% 
  as.dendrogram %>%
  set("branches_k_color", 
      value = c("red", "orange","blue", "green"), 
      k = 4)

#plot dendogram
dend %>% plot(main = "MFV Dendrogram")

Figure 8.1. The MFV dendogram based on 8 variables identified by PCA

(9) Accessions map and location correlation

# Correlation between mean MFV (of 8 important ) and elevation 
Elev_Cor <- as.data.frame(MFV$Mean)
Elev_Cor$Elevation <- Acc_loc$Elevation

cor_1 <- rcorr(as.matrix(Elev_Cor))
cor_1_result <- paste(round(cor_1$r[2,1], 2), sep = " - correlation coefficient")
knitr::kable(cor_1_result, caption = "Table 9.1. Correlation of mean MFV and elevation", align="c")

Table 9.1. Correlation of mean MFV and elevation

x
-0.07

Discussion

From your objectives.

The priliminary results, despite being slightly different from that in the article produced the ranking that corresponds to the groups (Highly tolerant - Highly sensitive) outlined in the article.

Conclusions

The analysis is not yet completed, so conclusions will be drawn later.